DNA Methylation and Prospects for Predicting the Therapeutic Effect of Neoadjuvant Chemotherapy for Triple-Negative and Luminal B Breast Cancer

Simple Summary Breast cancer (BC) is a group of diseases heterogeneous in morphology, progression, survival, and response to therapy. Although BC is among the most exhaustively studied cancers, there is still a lack of molecular markers to predict its response to neoadjuvant chemotherapy (NACT). Tumor development is determined by alterations not only of its genome, but of its epigenome as well. In order to identify epigenomic markers of BC NACT effectiveness, we have applied genome-wide DNA methylation screening of tumors in cohorts of NACT responders and nonresponders. Combining several of the most informative DNA methylation markers, we suggest tiny diagnostic panels that may be readily implemented in diagnostic laboratories. We also demonstrate that clinical characteristics predictive of NACT response, such as the clinical stage and lymph node status, are independently additive to the epigenetic classifiers and in combination improve prediction. Despite advances in the diagnosis and treatment of breast cancer (BC), the main cause of deaths is resistance to existing therapies. An approach to improve the effectiveness of therapy in patients with aggressive BC subtypes is neoadjuvant chemotherapy (NACT). Yet, the response to NACT for aggressive subtypes is less than 65% according to large clinical trials. An obvious fact is the lack of biomarkers predicting the therapeutic effect of NACT. In a search for epigenetic markers, we performed genome-wide differential methylation screening by XmaI-RRBS in cohorts of NACT responders and nonresponders, for triple-negative (TN) and luminal B tumors. The predictive potential of the most discriminative loci was further assessed in independent cohorts by methylation-sensitive restriction enzyme quantitative PCR (MSRE-qPCR), a promising method for the implementation of DNA methylation markers in diagnostic laboratories. The selected most informative individual markers were combined into panels demonstrating cvAUC = 0.83 (TMEM132D and MYO15B markers panel) for TN tumors and cvAUC = 0.76 (TTC34, LTBR and CLEC14A) for luminal B tumors. The combination of methylation markers with clinical features that correlate with NACT effect (clinical stage for TN and lymph node status for luminal B tumors) produces better classifiers, with cvAUC = 0.87 for TN tumors and cvAUC = 0.83 for luminal B tumors. Thus, clinical characteristics predictive of NACT response are independently additive to the epigenetic classifier and in combination improve prediction.